mpw-007 / NTM N-tip Implant

the NTM shapes of all of mpw-007

40 of 40 dies rendered.

138,244 shapes in total, each die at 0.125 µm per pixel.

result.json · job.json · reticle overview PNG

1234567
A A1 A2 A3 A4 A5 A6
B B1 B2 B3 B4 B5 B6 B7
C C1 C2 C3 C4 C6 C7
D D1 D2 D3 D4 D5 D6 D7
E E1 E2 E3 E4 E5 E6 E7
F F1 F2 F3 F4 F5 F6 F7

Downloading the full-resolution image

The stitched reticle is 53,432×65,056 pixels — 3.48 gigapixels. The file itself is small, but decoding it needs about 3.5 GB of memory however small the file is, and most viewers refuse it outright. So it is also published as 42 tiles of about 7576×10776 px (8.0 MB in total, largest 275.0 kB), cut in the gaps between dies so that every tile holds whole projects and none is split.

To put the full-resolution PNG back together from them, byte for byte, with nothing installed but Python itself:

python3 reconstruct_reticle.py

Run it in a directory holding the tiles and mpw-007_NTM_tiles.json and it finds them itself, checks every tile against its checksum as it reads it, and checks the finished file against the manifest. It works because the tiles and the full image are written with PNG's Up filter, which never looks at the pixel to its left — so a row of tiles' scanlines laid end to end is the full image's scanline, and rebuilding moves bytes rather than doing pixel arithmetic.

Whole-reticle files

FileSizeMD5SHA-1SHA-256
mpw-007_NTM_reticle.png
the stitched reticle, full resolution
5.8 MB a4d61f1ded997bb66489c0b104b706845252c3ba496edae5931b2e5ba6fb6b4104c59bd3 fd33d450479aa174e71b5120e3471b3b5fe6401cbf4c5420235a42beaca92a75
mpw-007_NTM_reticle_overview.png
the same image scaled to fit 4096 px
636.2 kB 03bb9264cd2053cefd322d761c26a0713715a0aa98d454f9fa7cdfeec9e1c2b3bdb4acaa 94d7daf9d6616be4ba95336d85418f70e1e83ee60c9057ff954b6384dc8a4433
mpw-007_NTM_tiles.json
where every tile belongs, and its checksum
13.5 kB f8bc3343279abe963dc665fbf9c3ffd6f18ed5e2142ac0837bef72b39607c78624387de1 ffac64d024c791fb78b71d0509e6ce59e8f7660d709f433b672f2bd6d727ad37
reconstruct_reticle.py
rebuilds the reticle from the tiles, standard library only
11.9 kB 4406f1f3db8a4a85756610b7c432c47a294714566ceb084187594ced0020f9260faac32a d2e72bf4b2541f1a8acb11eb758917c5b5fa080b41b3a7118487c03f8af2a769

Every file in this directory is listed in MD5SUMS · SHA1SUMS · SHA256SUMS, in the format the standard tools read:

sha256sum -c SHA256SUMS

The 42 tiles

FrameProjectPixelsPositionSizeSHA-256
A1 tinytapeout 7576×107760, 0 210.1 kB452173f8dfa27e0f15bfe4177a7fcba7fc9fc04c7fb316417710f3c2ed517b46
A2 zero_to_asic_course_group_submission_mpw7 7576×107767576, 0 188.9 kB522bda2035bd986d067035a26cb04e2c64ced8ad51913db1418c23c97b6bf934
A3 microwatt_mpw7 7576×1077615152, 0 208.9 kB115532e03db16905943dc0d09b0e0889afa9b9d226db26d8377aa82a33eb326c
A4 yonga-can_controller 7576×1077622728, 0 174.0 kB1fcfcf42d684ea74a1b351de0ff4468037b4da3f2e1834789241d004e47c8ca5
A5 rocketalpha 7576×1077630304, 0 224.5 kBa5cc7e916e331b52791ba575720fbc3960910fcbd6687305686d350b389a9f9a
A6 riscduino-dcore_d3_3 7576×1077637880, 0 226.0 kB6a866516e31e683d479cb689bcd2a79bafd75bea21b00c4a9c03a1033993b79f
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 riscduino-qcore_q2_3 7576×107760, 10776 231.5 kB3c3be766a0d694ff11c49e7e038b1267e8e12ca9a14ed20b22d7b27203bb2e9d
B2 riscduino-score-s4-3 7576×107767576, 10776 224.6 kBab1ef0356ae9416d858f247201d33e2c2834277a67222ed0b296eb573cf3fdc0
B3 nanofabrication_project_using_openfasoc 7576×1077615152, 10776 166.3 kB53ac8bc528a0e9621149f17da09439e47904c33f4586281d3e98a13a011dfb20
B4 sramtestvehicleg3 7576×1077622728, 10776 174.1 kB6809a95f6fbbd4e3c716592e8fea7c958b8ecfa4b80f48c7b2a2a05ee538f7c7
B5 analog_frontend_for_particle_detection_resubm 7576×1077630304, 10776 167.5 kB7eb14885df10989f492c43ae9869a7ec64ae998c09e1667fc542de10e618b9b2
B6 isa_16-bit_microprocessor 7576×1077637880, 10776 182.4 kB0b72a9cd3d195452a28cc70df247a5272ee87770794198e90f62ba4e09131d73
B7 reram_crossbar 7976×1077645456, 10776 217.2 kB9b0d5cd0bc0bf0e64aa2ca1c59c68b1983f335131bcbfb7abf6842159e39ae4c
C1 trainable-nn 7576×107760, 21552 180.8 kB94a2bfbbc1483f80bd054ad20e28c6a13e9149236ec96667305f09c0a812ceb8
C2 10b_adc_and_analog_support_-_update 7576×107767576, 21552 167.9 kB0879128fa2455b4d83f4c9e83aa1c25a91d7e82c120fb283a7c1872f7fbe3c8a
C3 bitcoin_mining_asics 7576×1077615152, 21552 174.0 kBe169f8eb7027d9914a2cbaad20108ae4c6dc88112cb38396c91a7964654bf7b9
C4 yonga-mcu 7576×1077622728, 21552 192.9 kB19898b6f6a5b11eb46b7c6abb8707eb8458c5940148bf2ff828350151ff90900
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 soomrv 7576×1077637880, 21552 203.1 kBdbee1fba74f6257b1f289f5374c9fc56dee82b13004dbf6af87ba62a4d866225
C7 marmot_risc-v_asic_for_motor_control 7976×1077645456, 21552 238.0 kB3ceb0b77df2152e41768b13a14ead02d683c509f55e9391fd07b17abf440cde5
D1 mixed_signal_circuits-jun13 7576×107760, 32328 166.1 kB339816751d851a97e821c7dee1696d5ade9b75c3a502e62ba64caf7e49d86001
D2 fpga_programming_management_unit 7576×107767576, 32328 221.1 kB0706b3dd6fd824f09ba4aec4a0f0234e730ab12efcf2a455db9e427f6fca0284
D3 warp-v 7576×1077615152, 32328 179.8 kB03410153385ccb5e6b29eca85e9d5ee6dc02df44a6479f7998ea6c9dcf049f6f
D4 leaf__mpw7_ 7576×1077622728, 32328 177.6 kB75daadc7916e1d35bc88bd8adba64d04d2dd86e0269af3678540931738800e0e
D5 in_memory_computing_sram 7576×1077630304, 32328 178.0 kBa620945e4730c406d1245ee3684cfca219ef5ba68b6b4e9f9298575939e032ac
D6 rng_based_on_a_figaro_oscillator 7576×1077637880, 32328 174.5 kB37941ff9293ebb1eb346966716e5ee18b0f9deb45dd3fcf18c923faab4a9dbf9
D7 reram-controller-mpw7_v2 7976×1077645456, 32328 176.0 kBbed3a3f6ab6745883afd561e7bb06b1291d62ee92dc71f0103764d496f5561d8
E1 enhanced_chaotic_oscillator_design 7576×107760, 43104 174.0 kB1fcfcf42d684ea74a1b351de0ff4468037b4da3f2e1834789241d004e47c8ca5
E2 in_memory_computing_rram 7576×107767576, 43104 181.5 kBdd09a3973dac5ff901265d723a0f9baac0bd2c194389772fa36fbe2d2d63d405
E3 rram_imc_v2_flat 7576×1077615152, 43104 181.5 kBdd09a3973dac5ff901265d723a0f9baac0bd2c194389772fa36fbe2d2d63d405
E4 topmetalse-dps 7576×1077622728, 43104 166.3 kBbb9d4688d95464ef82ff76d990f8cc6e1f6e018d70d16ed04ea9dd9ff1a9c21a
E5 chaos_automaton 7576×1077630304, 43104 242.8 kBfb8c5cebfc5ed8458acce749a5fbfdf3ecf6ea1674cb4af86399641925392a22
E6 hehecore_resubmission 7576×1077637880, 43104 220.5 kB7b155ae4303ebcae03395036b9327882a4989a543bce5b350715b98f674b8aae
E7 waveform_generator 7976×1077645456, 43104 183.9 kB7d044d0eedf27c1e4db0a0f5968b304ba1ecc68a728f9b784986cfa41979d43f
F1 rift2core 7576×111760, 53880 181.7 kB604f08ac6575eabb872b1c5eb201c947327b454f1f46e13a21bb03ff71f94393
F2 rift2go_2300 7576×111767576, 53880 188.0 kBa018953998dc5f4e9c3896d8cf22cdf6f665371658c3a87680156d6bcba7e0de
F3 sloci_resubmission 7576×1117615152, 53880 171.6 kBa6c6e8dbadc2e75d2dd58242f03e3331a307b63fa918577e9dd5edd1ae41325f
F4 systolic_array_matrix_multiplier 7576×1117622728, 53880 177.9 kBb76440807c5bf4f44bf467f28440d5d99aebe72702475743f361060ef91a37b3
F5 crypto_aes128 7576×1117630304, 53880 181.6 kB1420b3f0083e03409fc055208a2893ca1ff6d11fea6bc3820672e2cc463b10f1
F6 graphics_controller 7576×1117637880, 53880 192.5 kBe399f147aa58b52a3dcb7eb30507d37020d093e619436d17997e4dc8d1abc362
F7 prga-test 7976×1117645456, 53880 275.0 kBf0aad19174174da72f71ddfee328a4aa5f1652b3c2b863e2bae69acf9e84160f

Position is the tile's top-left corner in the full-resolution image. MD5 and SHA-1 for the same files are in the checksum files linked above.

About NTM on mpw-007

Resolved: reticle 5CS8016AC, run mpw-007, mask NTM

Mask layer
NTM — N-tip Implant
Process step
64 — NTM mask(tip fomation)
Drawn layers
64:20, 75:20, 11:44
Derived from drawn layers as
64:20 OR 11:44 OR (75:20 NOT 81:2)
Layer on the mask-layer GDS
27:0
Detail
created over nwell, ldntm and hvi (outside areaid.ce; rpm too in some flows) (cntm 26:20 is the fab's precomputed layer, not drawn)
Used in SKY130
yes

mpw-007 — SKY130 MPW-7

Reticle set
5CS8016AC
Run date
2022-07-08
Submissions closed
2022-09-12
Projects
106
Identification
inferred — Sequence after 5CS8014AC=MPW-6 (8018 is the re-made MPW-4 set); packing list 2026-08-25
Shuttle page
archived copy of https://platform.efabless.com/shuttles/MPW-7 — the platform that hosted it is gone
Foundry data
third_party/shuttle/sky130/mpw-007/foundry

These are renders of the drawn shapes that go on each photomask, taken from the layouts the shuttle's projects taped out — they are not photomask artwork: reticle pitch, the 4x reduction, mirroring and the frame features the fab adds are not modelled. An empty image is a real result; several mask layers are used by no project on a given shuttle. Each die's source layout is named in result.json and lives in the public shuttle repositories under foss-eda-tools.googlesource.com, so every image here can be traced back to the tape-out it came from. Generated with sky130-mask-decoder.