mpw-007 / INDM Inductor-TLM

the INDM shapes of all of mpw-007

40 of 40 dies rendered.

19,808,308 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 (53.3 MB in total, largest 2.3 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_INDM_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_INDM_reticle.png
the stitched reticle, full resolution
29.6 MB 17382a1caab1657aa622b4e4571a0b65c29df474f8e75276f0e1c81fd75b8d03f9176ded afeb9bce5e280b3c0a90f4197fdb1818c5688404ba824ee4c3f036ee4c065618
mpw-007_INDM_reticle_overview.png
the same image scaled to fit 4096 px
2.7 MB 46e35cec13ea706d71d222dfd8866cb70f3f081c8a098d57c1073958bea96fe99c7a808d 46fe605734231dea52c72d1d7ec11cd8eff90ccc4b6a2e590c62d1dfdebd42d7
mpw-007_INDM_tiles.json
where every tile belongs, and its checksum
13.6 kB afa33ab5221d6fdf3f2b2c60843c3b93153e473b6a27db419d8b33d1775440e0193aa691 066411481621dd75c0c40ddd405759707c685260bbcb4e048031019fa4b921d3
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 1.2 MBcab352c749259f9e3821119a69eec7b2abcf4b3a9f7208e2d1e19d8ebe8b5f6c
A2 zero_to_asic_course_group_submission_mpw7 7576×107767576, 0 1.7 MB00bc213140d65cce1d9b2e2794c5c4ef0b1b03e237ff75991c533e636cf18057
A3 microwatt_mpw7 7576×1077615152, 0 2.3 MB2560cb54c3a5da8d053ada888d581e12774f07ef15d6fb93c0f91845e08393f1
A4 yonga-can_controller 7576×1077622728, 0 1.0 MB0b069445a1411486932cf5b73bea5566412bf08fad00f94a25d3c340ada60941
A5 rocketalpha 7576×1077630304, 0 1.8 MB8dfa679ab75e3a2efa127b7605ef5a6d4609cbf930c0e99ddfe241a96464fbba
A6 riscduino-dcore_d3_3 7576×1077637880, 0 2.0 MB78a11249abe346423ddbe5aa2c0e3cbe5a78ad09a61e0269c284fc1a2465cdae
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 riscduino-qcore_q2_3 7576×107760, 10776 2.1 MBf6514fd7ec2562683830443367e5f7b207fa3df03d5984aa872a8594058d4ba1
B2 riscduino-score-s4-3 7576×107767576, 10776 1.9 MB4735ef84e1b1375d231b1e1b8bc8af6e3c7b4fc85a17a1c3c1b2e7df809754a9
B3 nanofabrication_project_using_openfasoc 7576×1077615152, 10776 661.0 kB67967d09c95b5bcb721faae4902e92de6cbdb561b6a7959235b7e7dea1be8997
B4 sramtestvehicleg3 7576×1077622728, 10776 967.5 kB598d384632014c27c683c88555558cef82d6fe9b260af76376b388146e670576
B5 analog_frontend_for_particle_detection_resubm 7576×1077630304, 10776 979.7 kBfabecd7c67dce8c3843891ece4692df21d076b234d0a8d0212d889372f903405
B6 isa_16-bit_microprocessor 7576×1077637880, 10776 1.1 MB4ddfa7a5e9e2db061910056fe806750a49028829d5e0a4d94b6f905e0eaf2fd4
B7 reram_crossbar 7976×1077645456, 10776 1.6 MB1fdf85481118ffa4af59b710ec2a99b8cf7f707df3fd436f2a57b3bbfe0062ae
C1 trainable-nn 7576×107760, 21552 1.5 MBf69eefa86f6baf4f996b2bd00d612bf69571d06dd8c2107dac2ebefd66f39744
C2 10b_adc_and_analog_support_-_update 7576×107767576, 21552 983.8 kBaf1d50116dbd0694878c88d6d8abe0d8a184e692bfff7a7ba9c2e601e08e7477
C3 bitcoin_mining_asics 7576×1077615152, 21552 1.1 MB004627c43d0fa4cb0694d7df16e5da120e494bd1a14d1fbf08aafc9de6099d7d
C4 yonga-mcu 7576×1077622728, 21552 1.6 MB7f826e48d3bf2a3c61fcd58d2e09bc98e6d91c2d51c7afbbf1502f5c734fad01
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 soomrv 7576×1077637880, 21552 2.1 MB551268d7275f5cffe33cde7e278b98a32ecdb3580eb9a9b9e1052b98898afce4
C7 marmot_risc-v_asic_for_motor_control 7976×1077645456, 21552 1.9 MBee5821d2746aa18d1878720ccc22954cd38a56f61afae516462939fa1f90a193
D1 mixed_signal_circuits-jun13 7576×107760, 32328 967.1 kBa49e80072102016b1fa8c2a155a51655b445446f3fd313a13c1847a9a6d51cbe
D2 fpga_programming_management_unit 7576×107767576, 32328 1.1 MBbf48e5322cd1c03f52e01a2b2d42c890064b459d492717260e905cf160ca76b4
D3 warp-v 7576×1077615152, 32328 1.2 MB9250a76c6360c5ce221a4af6a9f3ad7567ba5de3a505399a69576d44e8d0849b
D4 leaf__mpw7_ 7576×1077622728, 32328 1.1 MB0f94b419251c15fec19ae642204069b33ee7c53ae7a0283292379fc860965b54
D5 in_memory_computing_sram 7576×1077630304, 32328 1.0 MB57a78afada8dbf8cb2ab1c3b7370dbeff824abf94bd9a9b56982a89f0156d901
D6 rng_based_on_a_figaro_oscillator 7576×1077637880, 32328 1.0 MBba33ebf6a0fb56b478c6fa05d29a2d510a038a8f3b711c4f6d674967aebcc2ce
D7 reram-controller-mpw7_v2 7976×1077645456, 32328 1.0 MB0bbb56faf65bf3bd5db7cfe96d31ab6d0273b138f26046795078140c419704dd
E1 enhanced_chaotic_oscillator_design 7576×107760, 43104 1.0 MB882a52a7db6b07182c0ca0ba23899fb82bca178dee1d176c1a8a7eb7fd2b45c0
E2 in_memory_computing_rram 7576×107767576, 43104 1.1 MB8986f85a3ccdd5d37685d8ad1d221ee486b0e6ee4c5ba151cf3fd8fdadf86efd
E3 rram_imc_v2_flat 7576×1077615152, 43104 1.1 MB8986f85a3ccdd5d37685d8ad1d221ee486b0e6ee4c5ba151cf3fd8fdadf86efd
E4 topmetalse-dps 7576×1077622728, 43104 979.9 kB2f39a9377546d96fb78508fd33a4a6aa04ea121cdc70eec686f361ba56fcacfe
E5 chaos_automaton 7576×1077630304, 43104 1.9 MBeefb4c9070f0539ede4a0f673fbdc43c0ad70e006e8b38a1527b9226548267df
E6 hehecore_resubmission 7576×1077637880, 43104 1.5 MB3f8bd508786c5a75511495ebee74950be753c7c1e45d97fdc8346e8ba2c1c44b
E7 waveform_generator 7976×1077645456, 43104 1.1 MBa9824ad4fe46ee2802bfc109aa3819ea2301433621bb05f4615fb75dceed1868
F1 rift2core 7576×111760, 53880 1.0 MBa198747b4bc2fe6f68e1db10abef621645a02f367d434a1d3b39fd50d21353a9
F2 rift2go_2300 7576×111767576, 53880 1.7 MBf912e563c8620650a94a7c661d15f99f889812c534bfc9af916b9d172be80cce
F3 sloci_resubmission 7576×1117615152, 53880 983.2 kB759c77acad823005cca039a4f944bb5f8b367a6e6e5c90c47d734692d81202e3
F4 systolic_array_matrix_multiplier 7576×1117622728, 53880 1.0 MB6da94cf3a4972a7c6b155c4c19865a8329b105aedd51e3a965d550447d9ec406
F5 crypto_aes128 7576×1117630304, 53880 1.1 MB2c01189660a44745fcf0c75662e5496919feb43f0a8401f290c948457ccbf591
F6 graphics_controller 7576×1117637880, 53880 1.1 MBe302312039ab54236a6a577368f3b6494ee956399b240c07702f8a9c7a32da32
F7 prga-test 7976×1117645456, 53880 1.3 MB0fa784771c736cf0c5b2a88c15451df735f07ba2eef551afc9f8623eaf04a0ec

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 INDM on mpw-007

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

Mask layer
INDM — Inductor-TLM
Drawn layer
70:20
Detail
INDM = met3 (thick-last-metal flow, not SKY130)
Used in SKY130
no

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.