mpw-007 / VIM Via

the VIM shapes of all of mpw-007

40 of 40 dies rendered.

80,211,954 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 (94.6 MB in total, largest 4.9 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_VIM_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_VIM_reticle.png
the stitched reticle, full resolution
56.9 MB 5cd66c4b53bdec3c4b68676a6045c9003a30bba55e8588d1e15dd3a4889bf7938568be67 ddff726e82a17790f202bf310c7dd59c2e08620e72c218abbcfef62c3aca9a05
mpw-007_VIM_reticle_overview.png
the same image scaled to fit 4096 px
1.1 MB a9d170045173558caa7e2136782da34dbcca297c357084c2a9c5c1073f5dc771d0afd8d8 347284e1786dc9a9fa5659bc272f78b31f79a6f7ab8ba76671f70ab225650531
mpw-007_VIM_tiles.json
where every tile belongs, and its checksum
13.5 kB 05d371178894043c05cafd11623fab60ddc2e5127fea97e2d526ea2b44ba3c128edc9dc2 76e0e5feaee4a233b3355f02f3e4abbc6075ff2adc0fb9bcf09c414e41cffec9
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 2.0 MBd4c0f0513df6790f5c1cd13956e5448b1908613decfb1fb9e4dc8b3082f9fad9
A2 zero_to_asic_course_group_submission_mpw7 7576×107767576, 0 2.5 MB947353749ee91edf9217133b5f558e145a9a5be21efd1374c717b2ef61c17ade
A3 microwatt_mpw7 7576×1077615152, 0 3.9 MB191da93489cec25daec6b857156cf6c590c115e2cd9196391a3e9735249a0afa
A4 yonga-can_controller 7576×1077622728, 0 1.7 MBf5e048dff8f41dddb6d293577d5ea21bfc7708aa94faafdbd715e2de55ea7c1b
A5 rocketalpha 7576×1077630304, 0 3.3 MB41abdd1ec64d69e2bd7cab5ebf63fcda4b451466e1ba7801c2ed5f80e000a4bc
A6 riscduino-dcore_d3_3 7576×1077637880, 0 3.7 MBefdaaa64adb53cce69abce4009cbb7b69ef184f90d86f70a264514a314cca22f
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 riscduino-qcore_q2_3 7576×107760, 10776 3.7 MB314b917ff5d7ddf880a5b0d9855ab97c5c190bc6300834c6c678b9017fdd01bd
B2 riscduino-score-s4-3 7576×107767576, 10776 3.5 MBa3732a4cd34704c6383afef57e19020667d9a473a35c84d8be2266d8b10b4157
B3 nanofabrication_project_using_openfasoc 7576×1077615152, 10776 1.4 MB3d1f3b26e215240f38bf79510bbabf90a83ddf67121554a66f13d83460829371
B4 sramtestvehicleg3 7576×1077622728, 10776 1.6 MB3ef655a08a15f4c36e385e4625fd932db759d8c4090a929d7e2f9b186b0cc022
B5 analog_frontend_for_particle_detection_resubm 7576×1077630304, 10776 1.6 MB0d4e6e71aa958950593ea6a63b2f926ae8d4374902999e7fa9beafe0184b67fa
B6 isa_16-bit_microprocessor 7576×1077637880, 10776 1.7 MBfc8d9e5a5eac6201c285e6d0b237c6280587dc928d58c00a04a2c6703f7a1b42
B7 reram_crossbar 7976×1077645456, 10776 1.9 MBa5e9eb7c8c604f00fc1abf95b2c019142708fa5d8a28dd07f90300b843b654f1
C1 trainable-nn 7576×107760, 21552 2.9 MBdc23ae1277c634af0daf93170ade49a6821c1296865b6ec372765034fee158a5
C2 10b_adc_and_analog_support_-_update 7576×107767576, 21552 1.6 MBd7093a33f9d5b64765cbe03a0d8ea21f68e1ac108bd0434bc982d71e5cb2a803
C3 bitcoin_mining_asics 7576×1077615152, 21552 1.8 MBc1f0e36729be1d3feffd1c4bd3fb3ee4ebdf7975ec39fc5e29adc3b9c3f29f99
C4 yonga-mcu 7576×1077622728, 21552 2.7 MB1c07db20896c978a3f74b98eb71599a0de8d4002df50aba13194c664e467620a
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 soomrv 7576×1077637880, 21552 4.9 MBc49540deb65613a681623ac399d8bba64245d08b4e2a5e55bf87545ba933c4ba
C7 marmot_risc-v_asic_for_motor_control 7976×1077645456, 21552 3.1 MB3a534f713ea079350fb5407ad8821afd3452966469555ecc561f594dc42b0ba6
D1 mixed_signal_circuits-jun13 7576×107760, 32328 1.6 MB70049782647ca53ca5d439f6ccf5d1787a3940b624f953a82fe50c60445b05b1
D2 fpga_programming_management_unit 7576×107767576, 32328 1.7 MBe681fd1a6368a088bcc5ec5e83a91ec442b45bb5ebaea3f3c183481fc2ad076d
D3 warp-v 7576×1077615152, 32328 1.9 MB19ff8e051b9d815393b93283762870134be68a602c8378c1e36e7babac0a1df3
D4 leaf__mpw7_ 7576×1077622728, 32328 2.0 MB08b5718c03f3640d01484b4eb256b0ea122b0f590512077e205ede55a947415a
D5 in_memory_computing_sram 7576×1077630304, 32328 1.8 MBbb2da8fca3d2a6bb63cb9c04e8146b84b88bd46f4e62e2428b2023563e05d86f
D6 rng_based_on_a_figaro_oscillator 7576×1077637880, 32328 1.7 MB5d51fd66b5ca61a13486e8dd31cc86e2b637f8643427cf70839fce6d6569455e
D7 reram-controller-mpw7_v2 7976×1077645456, 32328 1.7 MBf69fcd0e579129f7295a90ba466dd028e632833fda576f526131a0439acbb52c
E1 enhanced_chaotic_oscillator_design 7576×107760, 43104 1.7 MB8ef4e73b8ed209d3bade843713f1165f4d1f5db5cf3e7cfa6b1ea808ed455d61
E2 in_memory_computing_rram 7576×107767576, 43104 1.8 MB4b92f57f49f8d9aa447022ef26768b9f5c5f54052c0411118d63848297afbfc1
E3 rram_imc_v2_flat 7576×1077615152, 43104 1.8 MB4b92f57f49f8d9aa447022ef26768b9f5c5f54052c0411118d63848297afbfc1
E4 topmetalse-dps 7576×1077622728, 43104 1.6 MB65e9a4cdb40eb7894cd9dff5f1939ee306d5bf72369b61fd12bd7f3262256faa
E5 chaos_automaton 7576×1077630304, 43104 4.2 MB19bb2151d443fea9a9a8eb8e0279dcb5c7cd48ce206bc70241c4260b27d5123a
E6 hehecore_resubmission 7576×1077637880, 43104 2.5 MB09de1411868b55fd07c5b7befc23111c19430366a528865611c3cc7e17ba6b9b
E7 waveform_generator 7976×1077645456, 43104 1.9 MB9df918c62aa96cb7a6401da34be2de42c275add003b406c4931c8b408f574f32
F1 rift2core 7576×111760, 53880 1.8 MB6ce3cf9cb8d454885ec72c8fffffa91573cfc7d2cca769decc466caa0ebaf695
F2 rift2go_2300 7576×111767576, 53880 4.4 MB3956b202b5886861db21b001b2c0c7f3babd9548320944185967c0f553b0dc35
F3 sloci_resubmission 7576×1117615152, 53880 1.6 MBceeae0ce3fcdfc02ebeda1648b3ab4d29e79405f89653489baac1c9b93b19c92
F4 systolic_array_matrix_multiplier 7576×1117622728, 53880 1.7 MBf1c15ae6ae2ca199defa4371f4f8ec85dc6d6ac0b2d24d0a9c713ac29a309e03
F5 crypto_aes128 7576×1117630304, 53880 2.1 MB58c46ef6eb2628255fce1ea578d4fda96265843ca1791a58ad3224575e1f23a3
F6 graphics_controller 7576×1117637880, 53880 1.9 MB360b9dfae8c9ac2a08b92c04786eb61515d498800e66e6311e8aa9b0f8e22f3e
F7 prga-test 7976×1117645456, 53880 3.4 MB3e5b4bceaefd085a8b741f921fc044b6381f6e681c221cefe5bc613b4f6e9d00

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 VIM on mpw-007

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

Mask layer
VIM — Via
Process step
118 — Via1 mask
Drawn layer
68:44
Layer on the mask-layer GDS
40:0
Detail
in the sky130B RRAM tier this is cviam, the lower half of the split via1; the upper half is VIMC (r1v)
Also
Via 1 (M1→M2)
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.