mpw-007 / MM4 Metal 4

the MM4 shapes of all of mpw-007

40 of 40 dies rendered.

217,590,560 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 (368.1 MB in total, largest 12.3 MB), 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_MM4_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_MM4_reticle.png
the stitched reticle, full resolution
206.0 MB c230d8e4902e85894d3098ddb5d9ccf22232042fe88b5aa1f4e93d577242dec805e6e680 5147684fb12cf0c9aecebdb82f35a158d314b4562779ff71619a4111a9743d29
mpw-007_MM4_reticle_overview.png
the same image scaled to fit 4096 px
6.2 MB 7ef259a609f353cd584d4095f44b0f20d392431f7c5c5e6bbc540543d7fd35e34635fd7f 2f420890f84a5da5e1a7c2513745c9232001f3d9fe26966d10f3761e6f5d8ff5
mpw-007_MM4_tiles.json
where every tile belongs, and its checksum
13.5 kB 20682352201a166f06f28a5258423d427657b018b015e628b655814ff1b921bfefca222b b143d752d97b1f067d0a5a443a4d4560f2cf97625807767e6795a6c17bf9542c
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 6.8 MBa0394cc73351a2a989f59bf86a258a7c2b265ff832ac83c0a63a322ce1022544
A2 zero_to_asic_course_group_submission_mpw7 7576×107767576, 0 11.2 MBb1ed7cd674e99f47b882e846e56da66a041f3ce8969a6bb673eddc656176b404
A3 microwatt_mpw7 7576×1077615152, 0 11.8 MBf028d4702804b42f68bb2a5f9b1879188d6f4963339dec3ce0fdd74c6e0dcdc0
A4 yonga-can_controller 7576×1077622728, 0 10.9 MBac08073a86ea8508a1266ada0919ff29929cbe1bbb5a2426b45bd65e29875751
A5 rocketalpha 7576×1077630304, 0 10.7 MB99be3fae22209849a88d7da9103a560eadde211eec58d25383a4de851c6de908
A6 riscduino-dcore_d3_3 7576×1077637880, 0 11.0 MB525ae2d0ede72855f2456225b6d3bceb973e0d54168105cd0f17f7acc7ffbfc4
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 riscduino-qcore_q2_3 7576×107760, 10776 11.4 MBba2b065cccbf9010a9e94cdc0ef4ef05bf20fb795d83b6153aebf132e85ad10b
B2 riscduino-score-s4-3 7576×107767576, 10776 11.0 MB032ea7478664dd7212e5f0c849aa14e68e52f571ba3c33556deceba99657e191
B3 nanofabrication_project_using_openfasoc 7576×1077615152, 10776 4.7 MBc07d3d5cb60a913fda9222f049871f9458bcb26c77b08320fbb82bcecbe563b9
B4 sramtestvehicleg3 7576×1077622728, 10776 3.6 MBe10c2f8b89661bc9e974e171fbeca063c326795b1e8d0c4992ae7b47560c9382
B5 analog_frontend_for_particle_detection_resubm 7576×1077630304, 10776 3.8 MB764d43e305d015b71c894d72558a7486415ca737dd940fcd2a06e6f0c35c38ce
B6 isa_16-bit_microprocessor 7576×1077637880, 10776 10.3 MB35f3325937914fdc2f3373ab011a7d5d592f7f389afe53e9f731900eda81a413
B7 reram_crossbar 7976×1077645456, 10776 6.8 MB54516ed04e596936e4859e4c972a58bf9d5f92a4b474ad17b0f78a898b48b941
C1 trainable-nn 7576×107760, 21552 11.5 MB2d0412dcde0db676b4d3a4f7098ad5e73370063c10e12cf353d3ba781cb3a761
C2 10b_adc_and_analog_support_-_update 7576×107767576, 21552 3.9 MBfddb492f137f9577a04bd2f4c05a6168db1fc38036e55a0812df15dd86a14892
C3 bitcoin_mining_asics 7576×1077615152, 21552 10.8 MBae48001539b099a6af8c6b3047a5531ef3bf7adde540a842b5da5a6f38e5f12f
C4 yonga-mcu 7576×1077622728, 21552 11.5 MBc5054a7df44e62a8e789bca87c22318d98c4ad7ca7d2da2631c3009778011215
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 soomrv 7576×1077637880, 21552 12.3 MBa1a0e9a4d85a753b9440f94e42e8ff7f94cb8747bf7a0128566978a389676be9
C7 marmot_risc-v_asic_for_motor_control 7976×1077645456, 21552 10.0 MBfea906db9a35e6f050a1fddca1042df9a33104f956aa9fda3bde524c816e70b9
D1 mixed_signal_circuits-jun13 7576×107760, 32328 3.6 MB9fc89d1633f82ab7fd0b4223494fc28cabf629c2d747d3a31c095502ce88975b
D2 fpga_programming_management_unit 7576×107767576, 32328 10.8 MBa7ad485b2055359d62dc057e7ff6bf327859faa58b318ba76eedcdb275a16a40
D3 warp-v 7576×1077615152, 32328 10.8 MB05567a22d157eb62810552dfa2b52994685867cc680f567462e4b39650704b83
D4 leaf__mpw7_ 7576×1077622728, 32328 10.8 MB53a068f76ecb2c959c953d3954fd7627e9d0a142faf063a72a80faf43d6dac95
D5 in_memory_computing_sram 7576×1077630304, 32328 10.7 MB32e23eab79df8056595d26d8bf184a1488caeb5006077a2ee18aa35261db152f
D6 rng_based_on_a_figaro_oscillator 7576×1077637880, 32328 10.9 MBc5989ac9a687ec47ce6c737f1fdfac9d240ffc687c5be3a27597cc33f1b8467a
D7 reram-controller-mpw7_v2 7976×1077645456, 32328 4.4 MB4fb74fcf5341d9811973b1857ebc7d4693c62f74051247238c96a8eec38c2efb
E1 enhanced_chaotic_oscillator_design 7576×107760, 43104 10.6 MBe83ba1e75dc10c46915229ebf5a78e5c6fdef3169d61ab251e215d649b0f79fe
E2 in_memory_computing_rram 7576×107767576, 43104 9.6 MBb199d7bc14f039b28c10225b578edf68aaaf4af4ac0b3fdab88cd7f70f10c7e3
E3 rram_imc_v2_flat 7576×1077615152, 43104 9.6 MBb199d7bc14f039b28c10225b578edf68aaaf4af4ac0b3fdab88cd7f70f10c7e3
E4 topmetalse-dps 7576×1077622728, 43104 4.2 MBdac33253939eec74345b7c08f6d202ed40c6260f954bd390cc254f81971651a5
E5 chaos_automaton 7576×1077630304, 43104 7.3 MBa0ef7364ad862cf86466d54cdb7873b480347cc9244c76a42b91fc3b76fc8ab8
E6 hehecore_resubmission 7576×1077637880, 43104 10.8 MBeac335a6cd91b610283fd1f75ad3afe7a140fca25d9311c261b89f8f38117b7c
E7 waveform_generator 7976×1077645456, 43104 10.7 MBa44ce183f5714de7fceb609b218d20abe39ca8dd7dc65a352114a59a172f3dea
F1 rift2core 7576×111760, 53880 11.0 MB8115c45059ee7cf1ac605262042ae0c507a40ac271a5d4734c4b2e5916762d3e
F2 rift2go_2300 7576×111767576, 53880 12.3 MBe65720dc27a02282278e63ac9881f5d265cebf6668e28ef9350a25e584bc8bfe
F3 sloci_resubmission 7576×1117615152, 53880 4.2 MB6a1129b1fc268e3ef6eec9a40af32c997ab1a1453ea46f5a232bb643cc439608
F4 systolic_array_matrix_multiplier 7576×1117622728, 53880 10.7 MB4280a63e5097ebe4a380511d4381e52f5a0d3f8b99b767cb90983f565f54db74
F5 crypto_aes128 7576×1117630304, 53880 10.0 MB065c64780c289a5f3c9927c20a6ec1fad07464a70b76aa37136541a3957a4503
F6 graphics_controller 7576×1117637880, 53880 10.8 MBb634163230a8a0ee47a0072237d2f51c7922a075f45355167c308704668b5380
F7 prga-test 7976×1117645456, 53880 9.9 MB1e2c62034c6919224b7fda263ca0124cb2e71387fb79f9c1dacdb5c2628be1a1

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 MM4 on mpw-007

Resolved: reticle 5CS8016AC, run mpw-007, mask MM4

Mask layer
MM4 — Metal 4
Process step
154 — Metal mask4
Drawn layer
71:20
Layer on the mask-layer GDS
51:0
Fill layer
51:28
Also
Metal 4
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.